DARPA Funds Neural Image Processor

Over the last eight years Lu's group has developed two types of direct-learning algorithms for memristors -- timing-based learning and weight-based learning.

University of Michigan professor Wei Lu (standing) works in the clean room with electrical engineering doctoral candidate, Siddharth Gaba.
(Source: Scott C. Soderberg, University of Michigan)

"We stimulate the network with images and the network self-adapts allowing its weights to evolve until a single neuron responds to a specific feature of the image, after which we can use the network to determine if a particular feature is present in any image," said Lu.

Funding for the first year of the project is set at $1.3 million, with new infusions each year during the first phase, which ends in 30 months with a prototype that can extract features from any image. The second phase aims to add a classifier that takes the features detected and recognizes combinations of them as particular objects, such as detecting the difference between a friendly F-15 jet and an adversary's MiG jet.

The layout of the memristor array (center) acting as the memory-synapses for the learning-neurons uses tungsten-oxide with vacancies that migrate when current flows thus changing a synapses strength.
(Source: University of Michigan)

Wei Lu is also a cofounder of Crossbar Inc. (Santa Clara, Calif.), which uses migrating silver ions in amorphous silicon to create resistive random access memories (ReRAM). But for his DARPA contract, instead of silver, he is casting his memristors in tungsten oxide, which changes its resistance as oxygen vacancies migrate from one end of the memristor to the other -- depending on which way the current is flowing -- thus acting as a resistance-based memory element.

All the work is being performed under the DARPA program called Unconventional Processing of Signals for Intelligent Data Exploitation. Lu's project is titled
Sparse Adaptive Local Learning for Sensing and Analytics. His collaborators include fellow professors Zhengya Zhang and Michael Flynn, Los Alamos National Lab scientist Garrett Kenyon, and Portland State University professor Christof Teuscher.

Interesting. So with such a fast image processor, we can have an additional warning inside our vehicles alerting the red signal in front of us. I have "missed" the red signal couple of times and luckily havent caused an accident till now. This would be a great help.